[D] Flowchart of 2023 AI Research Internship Search as a US PhD Student
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Data Monetization? Cue the Chief Data Monetization Officer
“Data Monetization! Data Monetization! Data Monetization!” Note: This blog was originally posted on December 12, 2017. But given all the recent excitement, I thought it might be time to revisit this blog. The original blog post was corrupted, so I re-posted the same content. It’s the new mantra of many organizations. But what does “data… Read More »Data Monetization? Cue the Chief Data Monetization Officer
The post Data Monetization? Cue the Chief Data Monetization Officer appeared first on Data Science Central.
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Improving Generalization in Game Agents with Data Augmentation in Imitation Learning. (arXiv:2309.12815v2 [cs.LG] UPDATED)
Imitation learning is an effective approach for training game-playing agents
and, consequently, for efficient game production. However, generalization - the
ability to perform well in related but unseen scenarios - is an essential
requirement that remains an unsolved challenge for game AI. Generalization is
difficult for imitation learning agents because it requires the algorithm to
take meaningful actions outside of the training distribution. In this paper we
propose a solution to this challenge. Inspired by the success of data
augmentation in supervised learning, we augment the training data so the
distribution of states and actions in the dataset better represents the real
state-action distribution. This study evaluates methods for combining and
applying data augmentations to observations, to improve generalization of
imitation learning agents. It also provides a performance benchmark of these
augmentations across several 3D environments. These results demonstrate that
data augmentation is a promising framework for improving generalization in
imitation learning agents.
( 2
min )
EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation. (arXiv:2312.13906v1 [cs.RO])
The use of autonomous robots for assistance tasks in hospitals has the
potential to free up qualified staff and im-prove patient care. However, the
ubiquity of deformable and transparent objects in hospital settings poses
signif-icant challenges to vision-based perception systems. We present
EfficientPPS, a neural architecture for part-aware panoptic segmentation that
provides robots with semantically rich visual information for grasping and
ma-nipulation tasks. We also present an unsupervised data collection and
labelling method to reduce the need for human involvement in the training
process. EfficientPPS is evaluated on a dataset containing real-world hospital
objects and demonstrated to be robust and efficient in grasping transparent
transfusion bags with a collaborative robot arm.
( 2
min )
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits. (arXiv:2301.11442v3 [cs.LG] UPDATED)
In this paper, we study the collaborative learning model, which concerns the
tradeoff between parallelism and communication overhead in multi-agent
multi-armed bandits. For regret minimization in multi-armed bandits, we present
the first set of tradeoffs between the number of rounds of communication among
the agents and the regret of the collaborative learning process.
( 2
min )
pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. (arXiv:2312.12337v2 [cs.CV] UPDATED)
We introduce pixelSplat, a feed-forward model that learns to reconstruct 3D
radiance fields parameterized by 3D Gaussian primitives from pairs of images.
Our model features real-time and memory-efficient rendering for scalable
training as well as fast 3D reconstruction at inference time. To overcome local
minima inherent to sparse and locally supported representations, we predict a
dense probability distribution over 3D and sample Gaussian means from that
probability distribution. We make this sampling operation differentiable via a
reparameterization trick, allowing us to back-propagate gradients through the
Gaussian splatting representation. We benchmark our method on wide-baseline
novel view synthesis on the real-world RealEstate10k and ACID datasets, where
we outperform state-of-the-art light field transformers and accelerate
rendering by 2.5 orders of magnitude while reconstructing an interpretable and
editable 3D radiance field.
( 2
min )
MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks. (arXiv:2312.13630v1 [cs.CV])
To better understand the output of deep neural networks (DNN), attribution
based methods have been an important approach for model interpretability, which
assign a score for each input dimension to indicate its importance towards the
model outcome. Notably, the attribution methods use the axioms of sensitivity
and implementation invariance to ensure the validity and reliability of
attribution results. Yet, the existing attribution methods present challenges
for effective interpretation and efficient computation. In this work, we
introduce MFABA, an attribution algorithm that adheres to axioms, as a novel
method for interpreting DNN. Additionally, we provide the theoretical proof and
in-depth analysis for MFABA algorithm, and conduct a large scale experiment.
The results demonstrate its superiority by achieving over 101.5142 times faster
speed than the state-of-the-art attribution algorithms. The effectiveness of
MFABA is thoroughly evaluated through the statistical analysis in comparison to
other methods, and the full implementation package is open-source at:
https://github.com/LMBTough/MFABA
( 2
min )
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments. (arXiv:2312.13896v1 [cs.LG])
This study explores the application of anomaly detection (AD) methods in
imbalanced learning tasks, focusing on fraud detection using real online credit
card payment data. We assess the performance of several recent AD methods and
compare their effectiveness against standard supervised learning methods.
Offering evidence of distribution shift within our dataset, we analyze its
impact on the tested models' performances. Our findings reveal that LightGBM
exhibits significantly superior performance across all evaluated metrics but
suffers more from distribution shifts than AD methods. Furthermore, our
investigation reveals that LightGBM also captures the majority of frauds
detected by AD methods. This observation challenges the potential benefits of
ensemble methods to combine supervised, and AD approaches to enhance
performance. In summary, this research provides practical insights into the
utility of these techniques in real-world scenarios, showing LightGBM's
superiority in fraud detection while highlighting challenges related to
distribution shifts.
( 2
min )
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation. (arXiv:2312.10423v2 [cs.LG] UPDATED)
Bayesian optimization (BO) is a sample-efficient method and has been widely
used for optimizing expensive black-box functions. Recently, there has been a
considerable interest in BO literature in optimizing functions that are
affected by context variable in the environment, which is uncontrollable by
decision makers. In this paper, we focus on the optimization of functions'
expectations over continuous context variable, subject to an unknown
distribution. To address this problem, we propose two algorithms that employ
kernel density estimation to learn the probability density function (PDF) of
continuous context variable online. The first algorithm is simpler, which
directly optimizes the expectation under the estimated PDF. Considering that
the estimated PDF may have high estimation error when the true distribution is
complicated, we further propose the second algorithm that optimizes the
distributionally robust objective. Theoretical results demonstrate that both
algorithms have sub-linear Bayesian cumulative regret on the expectation
objective. Furthermore, we conduct numerical experiments to empirically
demonstrate the effectiveness of our algorithms.
( 2
min )
Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property. (arXiv:2312.12067v2 [cs.GT] UPDATED)
Policy gradient methods enjoy strong practical performance in numerous tasks
in reinforcement learning. Their theoretical understanding in multiagent
settings, however, remains limited, especially beyond two-player competitive
and potential Markov games. In this paper, we develop a new framework to
characterize optimistic policy gradient methods in multi-player Markov games
with a single controller. Specifically, under the further assumption that the
game exhibits an equilibrium collapse, in that the marginals of coarse
correlated equilibria (CCE) induce Nash equilibria (NE), we show convergence to
stationary $\epsilon$-NE in $O(1/\epsilon^2)$ iterations, where $O(\cdot)$
suppresses polynomial factors in the natural parameters of the game. Such an
equilibrium collapse is well-known to manifest itself in two-player zero-sum
Markov games, but also occurs even in a class of multi-player Markov games with
separable interactions, as established by recent work. As a result, we bypass
known complexity barriers for computing stationary NE when either of our
assumptions fails. Our approach relies on a natural generalization of the
classical Minty property that we introduce, which we anticipate to have further
applications beyond Markov games.
( 2
min )
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries. (arXiv:2312.13671v1 [cs.CL])
Tabular data analysis is crucial in various fields, and large language models
show promise in this area. However, current research mostly focuses on
rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like
forecasting and chart generation. To address this gap, we developed the
Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond
the SQL-compatible operations and require more in-depth analysis. We also
develop five innovative and effective annotation methods, harnessing the
capabilities of large language models to enhance data quality and quantity.
Additionally, we include unclear queries that resemble real-world user
questions to test how well models can understand and tackle such challenges.
Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five
state-of-the-art models using three different metrics and the results show that
our benchmark presents introduces considerable challenge in the field of
tabular data analysis, paving the way for more advanced research opportunities.
( 2
min )
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models. (arXiv:2305.15194v2 [cs.CV] UPDATED)
In this study, we aim to extend the capabilities of diffusion-based
text-to-image (T2I) generation models by incorporating diverse modalities
beyond textual description, such as sketch, box, color palette, and style
embedding, within a single model. We thus design a multimodal T2I diffusion
model, coined as DiffBlender, by separating the channels of conditions into
three types, i.e., image forms, spatial tokens, and non-spatial tokens. The
unique architecture of DiffBlender facilitates adding new input modalities,
pioneering a scalable framework for conditional image generation. Notably, we
achieve this without altering the parameters of the existing generative model,
Stable Diffusion, only with updating partial components. Our study establishes
new benchmarks in multimodal generation through quantitative and qualitative
comparisons with existing conditional generation methods. We demonstrate that
DiffBlender faithfully blends all the provided information and showcase its
various applications in the detailed image synthesis.
( 2
min )
Best Arm Identification in Batched Multi-armed Bandit Problems. (arXiv:2312.13875v1 [stat.ML])
Recently multi-armed bandit problem arises in many real-life scenarios where
arms must be sampled in batches, due to limited time the agent can wait for the
feedback. Such applications include biological experimentation and online
marketing. The problem is further complicated when the number of arms is large
and the number of batches is small. We consider pure exploration in a batched
multi-armed bandit problem. We introduce a general linear programming framework
that can incorporate objectives of different theoretical settings in best arm
identification. The linear program leads to a two-stage algorithm that can
achieve good theoretical properties. We demonstrate by numerical studies that
the algorithm also has good performance compared to certain UCB-type or
Thompson sampling methods.
( 2
min )
AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization. (arXiv:2312.14027v1 [stat.ML])
Uncertainty estimation is a key issue when considering the application of
deep neural network methods in science and engineering. In this work, we
introduce a novel algorithm that quantifies epistemic uncertainty via Monte
Carlo sampling from a tempered posterior distribution. It combines the well
established Metropolis Adjusted Langevin Algorithm (MALA) with momentum-based
optimization using Adam and leverages a prolate proposal distribution, to
efficiently draw from the posterior. We prove that the constructed chain admits
the Gibbs posterior as an invariant distribution and converges to this Gibbs
posterior in total variation distance. Numerical evaluations are postponed to a
first revision.
( 2
min )
R\'enyi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration. (arXiv:2312.13985v1 [cs.CR])
Pufferfish privacy is a flexible generalization of differential privacy that
allows to model arbitrary secrets and adversary's prior knowledge about the
data. Unfortunately, designing general and tractable Pufferfish mechanisms that
do not compromise utility is challenging. Furthermore, this framework does not
provide the composition guarantees needed for a direct use in iterative machine
learning algorithms. To mitigate these issues, we introduce a R\'enyi
divergence-based variant of Pufferfish and show that it allows us to extend the
applicability of the Pufferfish framework. We first generalize the Wasserstein
mechanism to cover a wide range of noise distributions and introduce several
ways to improve its utility. We also derive stronger guarantees against
out-of-distribution adversaries. Finally, as an alternative to composition, we
prove privacy amplification results for contractive noisy iterations and
showcase the first use of Pufferfish in private convex optimization. A common
ingredient underlying our results is the use and extension of shift reduction
lemmas.
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Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering. (arXiv:2211.01877v2 [stat.ML] UPDATED)
Convex clustering is a modern method with both hierarchical and $k$-means
clustering characteristics. Although convex clustering can capture complex
clustering structures hidden in data, the existing convex clustering algorithms
are not scalable to large data sets with sample sizes greater than several
thousands. Moreover, it is known that convex clustering sometimes fails to
produce a complete hierarchical clustering structure. This issue arises if
clusters split up or the minimum number of possible clusters is larger than the
desired number of clusters. In this paper, we propose convex clustering through
majorization-minimization (CCMM) -- an iterative algorithm that uses cluster
fusions and a highly efficient updating scheme derived using diagonal
majorization. Additionally, we explore different strategies to ensure that the
hierarchical clustering structure terminates in a single cluster. With a
current desktop computer, CCMM efficiently solves convex clustering problems
featuring over one million objects in seven-dimensional space, achieving a
solution time of 51 seconds on average.
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A note on the connectedness property of union-free generic sets of partial orders. (arXiv:2304.10549v2 [cs.LG] UPDATED)
This short note describes and proves a connectedness property which was
introduced in Blocher et al. [2023] in the context of data depth functions for
partial orders. The connectedness property gives a structural insight into
union-free generic sets. These sets, presented in Blocher et al. [2023], are
defined by using a closure operator on the set of all partial orders which
naturally appears within the theory of formal concept analysis. In the language
of formal concept analysis, the property of connectedness can be vividly
proven. However, since within Blocher et al. [2023] we did not discuss formal
concept analysis, we outsourced the proof to this note.
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Statistical learning theory and Occam's razor: The argument from empirical risk minimization. (arXiv:2312.13842v1 [cs.LG])
This paper considers the epistemic justification for a simplicity preference
in inductive inference that may be obtained from the machine learning framework
of statistical learning theory. Uniting elements from both earlier arguments
suggesting and rejecting such a justification, the paper spells out a qualified
means-ends and model-relative justificatory argument, built on statistical
learning theory's central mathematical learning guarantee for the method of
empirical risk minimization.
( 2
min )
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion. (arXiv:2312.13616v1 [cs.LG])
Generating counterfactual explanations is one of the most effective
approaches for uncovering the inner workings of black-box neural network models
and building user trust. While remarkable strides have been made in generative
modeling using diffusion models in domains like vision, their utility in
generating counterfactual explanations in structured modalities remains
unexplored. In this paper, we introduce Structured Counterfactual Diffuser or
SCD, the first plug-and-play framework leveraging diffusion for generating
counterfactual explanations in structured data. SCD learns the underlying data
distribution via a diffusion model which is then guided at test time to
generate counterfactuals for any arbitrary black-box model, input, and desired
prediction. Our experiments show that our counterfactuals not only exhibit high
plausibility compared to the existing state-of-the-art but also show
significantly better proximity and diversity.
( 2
min )
Reversible and irreversible bracket-based dynamics for deep graph neural networks. (arXiv:2305.15616v3 [cs.LG] UPDATED)
Recent works have shown that physics-inspired architectures allow the
training of deep graph neural networks (GNNs) without oversmoothing. The role
of these physics is unclear, however, with successful examples of both
reversible (e.g., Hamiltonian) and irreversible (e.g., diffusion) phenomena
producing comparable results despite diametrically opposed mechanisms, and
further complications arising due to empirical departures from mathematical
theory. This work presents a series of novel GNN architectures based upon
structure-preserving bracket-based dynamical systems, which are provably
guaranteed to either conserve energy or generate positive dissipation with
increasing depth. It is shown that the theoretically principled framework
employed here allows for inherently explainable constructions, which
contextualize departures from theory in current architectures and better
elucidate the roles of reversibility and irreversibility in network
performance.
( 2
min )
Comparison of two data fusion approaches for land use classification. (arXiv:2311.07967v2 [cs.LG] UPDATED)
Accurate land use maps, describing the territory from an anthropic
utilisation point of view, are useful tools for land management and planning.
To produce them, the use of optical images alone remains limited. It is
therefore necessary to make use of several heterogeneous sources, each carrying
complementary or contradictory information due to their imperfections or their
different specifications. This study compares two different approaches i.e. a
pre-classification and a post-classification fusion approach for combining
several sources of spatial data in the context of land use classification. The
approaches are applied on authoritative land use data located in the Gers
department in the southwest of France. Pre-classification fusion, while not
explicitly modeling imperfections, has the best final results, reaching an
overall accuracy of 97% and a macro-mean F1 score of 88%.
( 2
min )
RLHF and IIA: Perverse Incentives. (arXiv:2312.01057v2 [cs.LG] UPDATED)
Existing algorithms for reinforcement learning from human feedback (RLHF) can
incentivize responses at odds with preferences because they are based on models
that assume independence of irrelevant alternatives (IIA). The perverse
incentives induced by IIA give rise to egregious behavior when innovating on
query formats or learning algorithms.
( 2
min )
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. (arXiv:2310.02679v2 [cs.LG] UPDATED)
We tackle the problem of sampling from intractable high-dimensional density
functions, a fundamental task that often appears in machine learning and
statistics. We extend recent sampling-based approaches that leverage controlled
stochastic processes to model approximate samples from these target densities.
The main drawback of these approaches is that the training objective requires
full trajectories to compute, resulting in sluggish credit assignment issues
due to use of entire trajectories and a learning signal present only at the
terminal time. In this work, we present Diffusion Generative Flow Samplers
(DGFS), a sampling-based framework where the learning process can be tractably
broken down into short partial trajectory segments, via parameterizing an
additional "flow function". Our method takes inspiration from the theory
developed for generative flow networks (GFlowNets), allowing us to make use of
intermediate learning signals. Through various challenging experiments, we
demonstrate that DGFS achieves more accurate estimates of the normalization
constant than closely-related prior methods.
( 2
min )
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines. (arXiv:2304.04273v2 [cs.LG] UPDATED)
Through this paper, we introduce a novel driver cognitive load assessment
dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with
other physiological signals such as Electrocardiography (ECG) and Electrodermal
Activity (EDA) as well as eye tracking data. The data was collected from 21
subjects while driving in an immersive vehicle simulator, in various driving
conditions, to induce different levels of cognitive load in the subjects. The
tasks consisted of 9 complexity levels for 3 minutes each. Each driver reported
their subjective cognitive load every 10 seconds throughout the experiment. The
dataset contains the subjective cognitive load recorded as ground truth. In
this paper, we also provide benchmark classification results for different
machine learning and deep learning models for both binary and ternary label
distributions. We followed 2 evaluation criteria namely 10-fold and
leave-one-subject-out (LOSO). We have trained our models on both hand-crafted
features as well as on raw data.
( 3
min )
Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load. (arXiv:2302.03616v3 [cs.LG] UPDATED)
The effectiveness of digital treatments can be measured by requiring patients
to self-report their state through applications, however, it can be
overwhelming and causes disengagement. We conduct a study to explore the impact
of gamification on self-reporting. Our approach involves the creation of a
system to assess cognitive load (CL) through the analysis of
photoplethysmography (PPG) signals. The data from 11 participants is utilized
to train a machine learning model to detect CL. Subsequently, we create two
versions of surveys: a gamified and a traditional one. We estimate the CL
experienced by other participants (13) while completing surveys. We find that
CL detector performance can be enhanced via pre-training on stress detection
tasks. For 10 out of 13 participants, a personalized CL detector can achieve an
F1 score above 0.7. We find no difference between the gamified and non-gamified
surveys in terms of CL but participants prefer the gamified version.
( 3
min )
Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering. (arXiv:2312.14066v1 [cs.LG])
Multi-relational clustering is a challenging task due to the fact that
diverse semantic information conveyed in multi-layer graphs is difficult to
extract and fuse. Recent methods integrate topology structure and node
attribute information through graph filtering. However, they often use a
low-pass filter without fully considering the correlation among multiple
graphs. To overcome this drawback, we propose to learn a graph filter motivated
by the theoretical analysis of Barlow Twins. We find that input with a negative
semi-definite inner product provides a lower bound for Barlow Twins loss, which
prevents it from reaching a better solution. We thus learn a filter that yields
an upper bound for Barlow Twins. Afterward, we design a simple clustering
architecture and demonstrate its state-of-the-art performance on four benchmark
datasets.
( 2
min )
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing. (arXiv:2312.14000v1 [cs.LG])
Stochastic optimal control of dynamical systems is a crucial challenge in
sequential decision-making. Recently, control-as-inference approaches have had
considerable success, providing a viable risk-sensitive framework to address
the exploration-exploitation dilemma. Nonetheless, a majority of these
techniques only invoke the inference-control duality to derive a modified risk
objective that is then addressed within a reinforcement learning framework.
This paper introduces a novel perspective by framing risk-sensitive stochastic
control as Markovian score climbing under samples drawn from a conditional
particle filter. Our approach, while purely inference-centric, provides
asymptotically unbiased estimates for gradient-based policy optimization with
optimal importance weighting and no explicit value function learning. To
validate our methodology, we apply it to the task of learning neural
non-Gaussian feedback policies, showcasing its efficacy on numerical benchmarks
of stochastic dynamical systems.
( 2
min )
Docking-based generative approaches in the search for new drug candidates. (arXiv:2312.13944v1 [q-bio.BM])
Despite the great popularity of virtual screening of existing compound
libraries, the search for new potential drug candidates also takes advantage of
generative protocols, where new compound suggestions are enumerated using
various algorithms. To increase the activity potency of generative approaches,
they have recently been coupled with molecular docking, a leading methodology
of structure-based drug design. In this review, we summarize progress since
docking-based generative models emerged. We propose a new taxonomy for these
methods and discuss their importance for the field of computer-aided drug
design. In addition, we discuss the most promising directions for further
development of generative protocols coupled with docking.
( 2
min )
On the convergence of loss and uncertainty-based active learning algorithms. (arXiv:2312.13927v1 [cs.LG])
We study convergence rates of loss and uncertainty-based active learning
algorithms under various assumptions. First, we provide a set of conditions
under which a convergence rate guarantee holds, and use this for linear
classifiers and linearly separable datasets to show convergence rate guarantees
for loss-based sampling and different loss functions. Second, we provide a
framework that allows us to derive convergence rate bounds for loss-based
sampling by deploying known convergence rate bounds for stochastic gradient
descent algorithms. Third, and last, we propose an active learning algorithm
that combines sampling of points and stochastic Polyak's step size. We show a
condition on the sampling that ensures a convergence rate guarantee for this
algorithm for smooth convex loss functions. Our numerical results demonstrate
efficiency of our proposed algorithm.
( 2
min )
Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming. (arXiv:2312.13905v1 [cs.RO])
Industrial robots are applied in a widening range of industries, but robot
programming mostly remains a task limited to programming experts. We propose a
natural language-based assistant for programming of advanced, industrial
robotic applications and investigate strategies for domain-specific fine-tuning
of foundation models with limited data and compute.
( 2
min )
Parallel Trust-Region Approaches in Neural Network Training: Beyond Traditional Methods. (arXiv:2312.13677v1 [math.NA])
We propose to train neural networks (NNs) using a novel variant of the
``Additively Preconditioned Trust-region Strategy'' (APTS). The proposed method
is based on a parallelizable additive domain decomposition approach applied to
the neural network's parameters. Built upon the TR framework, the APTS method
ensures global convergence towards a minimizer. Moreover, it eliminates the
need for computationally expensive hyper-parameter tuning, as the TR algorithm
automatically determines the step size in each iteration. We demonstrate the
capabilities, strengths, and limitations of the proposed APTS training method
by performing a series of numerical experiments. The presented numerical study
includes a comparison with widely used training methods such as SGD, Adam,
LBFGS, and the standard TR method.
( 2
min )
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction. (arXiv:2312.13558v1 [cs.LG])
Transformer-based Large Language Models (LLMs) have become a fixture in
modern machine learning. Correspondingly, significant resources are allocated
towards research that aims to further advance this technology, typically
resulting in models of increasing size that are trained on increasing amounts
of data. This work, however, demonstrates the surprising result that it is
often possible to significantly improve the performance of LLMs by selectively
removing higher-order components of their weight matrices. This simple
intervention, which we call LAyer-SElective Rank reduction (LASER), can be done
on a model after training has completed, and requires no additional parameters
or data. We show extensive experiments demonstrating the generality of this
finding across language models and datasets, and provide in-depth analyses
offering insights into both when LASER is effective and the mechanism by which
it operates.
( 2
min )
InvertibleNetworks.jl: A Julia package for scalable normalizing flows. (arXiv:2312.13480v1 [cs.LG])
InvertibleNetworks.jl is a Julia package designed for the scalable
implementation of normalizing flows, a method for density estimation and
sampling in high-dimensional distributions. This package excels in memory
efficiency by leveraging the inherent invertibility of normalizing flows, which
significantly reduces memory requirements during backpropagation compared to
existing normalizing flow packages that rely on automatic differentiation
frameworks. InvertibleNetworks.jl has been adapted for diverse applications,
including seismic imaging, medical imaging, and CO2 monitoring, demonstrating
its effectiveness in learning high-dimensional distributions.
( 2
min )
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent. (arXiv:2312.13486v1 [cs.LG])
Utilizing task-invariant prior knowledge extracted from related tasks,
meta-learning is a principled framework that empowers learning a new task
especially when data records are limited. A fundamental challenge in
meta-learning is how to quickly "adapt" the extracted prior in order to train a
task-specific model within a few optimization steps. Existing approaches deal
with this challenge using a preconditioner that enhances convergence of the
per-task training process. Though effective in representing locally a quadratic
training loss, these simple linear preconditioners can hardly capture complex
loss geometries. The present contribution addresses this limitation by learning
a nonlinear mirror map, which induces a versatile distance metric to enable
capturing and optimizing a wide range of loss geometries, hence facilitating
the per-task training. Numerical tests on few-shot learning datasets
demonstrate the superior expressiveness and convergence of the advocated
approach.
( 2
min )
Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic Processes. (arXiv:2312.13473v1 [quant-ph])
Many inference scenarios rely on extracting relevant information from known
data in order to make future predictions. When the underlying stochastic
process satisfies certain assumptions, there is a direct mapping between its
exact classical and quantum simulators, with the latter asymptotically using
less memory. Here we focus on studying whether such quantum advantage persists
when those assumptions are not satisfied, and the model is doomed to have
imperfect accuracy. By studying the trade-off between accuracy and memory
requirements, we show that quantum models can reach the same accuracy with less
memory, or alternatively, better accuracy with the same memory. Finally, we
discuss the implications of this result for learning tasks.
( 2
min )
Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround Airfoil Geometries. (arXiv:2312.13403v1 [cs.LG])
Physical based simulations can be very time and computationally demanding
tasks. One way of accelerating these processes is by making use of data-driven
surrogate models that learn from existing simulations. Ensembling methods are
particularly relevant in this domain as their smoothness properties coincide
with the smoothness of physical phenomena. The drawback is that they can remain
costly. This research project focused on studying Packed-Ensembles that
generalize Deep Ensembles but remain faster to train. Several models have been
trained and compared in terms of multiple important metrics. PE(8,4,1) has been
identified as the clear winner in this particular task, beating down its Deep
Ensemble conterpart while accelerating the training time by 25%.
( 2
min )
Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments. (arXiv:2312.13397v1 [physics.atom-ph])
The generation of cold atom clouds is a complex process which involves the
optimization of noisy data in high dimensional parameter spaces. Optimization
can be challenging both in and especially outside of the lab due to lack of
time, expertise, or access for lengthy manual optimization. In recent years, it
was demonstrated that machine learning offers a solution since it can optimize
high dimensional problems quickly, without knowledge of the experiment itself.
In this paper we present results showing the benchmarking of nine different
optimization techniques and implementations, alongside their ability to
optimize a Rubidium (Rb) cold atom experiment. The investigations are performed
on a 3D $^{87}$Rb molasses with 10 and 18 adjustable parameters, respectively,
where the atom number obtained by absorption imaging was chosen as the test
problem. We further compare the best performing optimizers under different
effective noise conditions by reducing the Signal-to-Noise ratio of the images
via adapting the atomic vapor pressure in the 2D+ MOT and the detection laser
frequency stability.
( 2
min )
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Open
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning. (arXiv:2305.19442v4 [cs.LG] CROSS LISTED)
Federated bilevel optimization (FBO) has shown great potential recently in
machine learning and edge computing due to the emerging nested optimization
structure in meta-learning, fine-tuning, hyperparameter tuning, etc. However,
existing FBO algorithms often involve complicated computations and require
multiple sub-loops per iteration, each of which contains a number of
communication rounds. In this paper, we propose a simple and flexible FBO
framework named SimFBO, which is easy to implement without sub-loops, and
includes a generalized server-side aggregation and update for improving
communication efficiency. We further propose System-level heterogeneity robust
FBO (ShroFBO) as a variant of SimFBO with stronger resilience to heterogeneous
local computation. We show that SimFBO and ShroFBO provably achieve a linear
convergence speedup with partial client participation and client sampling
without replacement, as well as improved sample and communication complexities.
Experiments demonstrate the effectiveness of the proposed methods over existing
FBO algorithms.
( 2
min )
Achieving ${O}(\epsilon^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization. (arXiv:2312.03807v2 [math.OC] CROSS LISTED)
In this paper, we revisit the bilevel optimization problem, in which the
upper-level objective function is generally nonconvex and the lower-level
objective function is strongly convex. Although this type of problem has been
studied extensively, it still remains an open question how to achieve an
${O}(\epsilon^{-1.5})$ sample complexity in Hessian/Jacobian-free stochastic
bilevel optimization without any second-order derivative computation. To fill
this gap, we propose a novel Hessian/Jacobian-free bilevel optimizer named
FdeHBO, which features a simple fully single-loop structure, a projection-aided
finite-difference Hessian/Jacobian-vector approximation, and momentum-based
updates. Theoretically, we show that FdeHBO requires ${O}(\epsilon^{-1.5})$
iterations (each using ${O}(1)$ samples and only first-order gradient
information) to find an $\epsilon$-accurate stationary point. As far as we
know, this is the first Hessian/Jacobian-free method with an
${O}(\epsilon^{-1.5})$ sample complexity for nonconvex-strongly-convex
stochastic bilevel optimization.
( 2
min )
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. (arXiv:2310.02679v2 [cs.LG] UPDATED)
We tackle the problem of sampling from intractable high-dimensional density
functions, a fundamental task that often appears in machine learning and
statistics. We extend recent sampling-based approaches that leverage controlled
stochastic processes to model approximate samples from these target densities.
The main drawback of these approaches is that the training objective requires
full trajectories to compute, resulting in sluggish credit assignment issues
due to use of entire trajectories and a learning signal present only at the
terminal time. In this work, we present Diffusion Generative Flow Samplers
(DGFS), a sampling-based framework where the learning process can be tractably
broken down into short partial trajectory segments, via parameterizing an
additional "flow function". Our method takes inspiration from the theory
developed for generative flow networks (GFlowNets), allowing us to make use of
intermediate learning signals. Through various challenging experiments, we
demonstrate that DGFS achieves more accurate estimates of the normalization
constant than closely-related prior methods.
( 2
min )
AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization. (arXiv:2312.14027v1 [stat.ML])
Uncertainty estimation is a key issue when considering the application of
deep neural network methods in science and engineering. In this work, we
introduce a novel algorithm that quantifies epistemic uncertainty via Monte
Carlo sampling from a tempered posterior distribution. It combines the well
established Metropolis Adjusted Langevin Algorithm (MALA) with momentum-based
optimization using Adam and leverages a prolate proposal distribution, to
efficiently draw from the posterior. We prove that the constructed chain admits
the Gibbs posterior as an invariant distribution and converges to this Gibbs
posterior in total variation distance. Numerical evaluations are postponed to a
first revision.
( 2
min )
Best Arm Identification in Batched Multi-armed Bandit Problems. (arXiv:2312.13875v1 [stat.ML])
Recently multi-armed bandit problem arises in many real-life scenarios where
arms must be sampled in batches, due to limited time the agent can wait for the
feedback. Such applications include biological experimentation and online
marketing. The problem is further complicated when the number of arms is large
and the number of batches is small. We consider pure exploration in a batched
multi-armed bandit problem. We introduce a general linear programming framework
that can incorporate objectives of different theoretical settings in best arm
identification. The linear program leads to a two-stage algorithm that can
achieve good theoretical properties. We demonstrate by numerical studies that
the algorithm also has good performance compared to certain UCB-type or
Thompson sampling methods.
( 2
min )
Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering. (arXiv:2211.01877v2 [stat.ML] UPDATED)
Convex clustering is a modern method with both hierarchical and $k$-means
clustering characteristics. Although convex clustering can capture complex
clustering structures hidden in data, the existing convex clustering algorithms
are not scalable to large data sets with sample sizes greater than several
thousands. Moreover, it is known that convex clustering sometimes fails to
produce a complete hierarchical clustering structure. This issue arises if
clusters split up or the minimum number of possible clusters is larger than the
desired number of clusters. In this paper, we propose convex clustering through
majorization-minimization (CCMM) -- an iterative algorithm that uses cluster
fusions and a highly efficient updating scheme derived using diagonal
majorization. Additionally, we explore different strategies to ensure that the
hierarchical clustering structure terminates in a single cluster. With a
current desktop computer, CCMM efficiently solves convex clustering problems
featuring over one million objects in seven-dimensional space, achieving a
solution time of 51 seconds on average.
( 2
min )
R\'enyi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration. (arXiv:2312.13985v1 [cs.CR])
Pufferfish privacy is a flexible generalization of differential privacy that
allows to model arbitrary secrets and adversary's prior knowledge about the
data. Unfortunately, designing general and tractable Pufferfish mechanisms that
do not compromise utility is challenging. Furthermore, this framework does not
provide the composition guarantees needed for a direct use in iterative machine
learning algorithms. To mitigate these issues, we introduce a R\'enyi
divergence-based variant of Pufferfish and show that it allows us to extend the
applicability of the Pufferfish framework. We first generalize the Wasserstein
mechanism to cover a wide range of noise distributions and introduce several
ways to improve its utility. We also derive stronger guarantees against
out-of-distribution adversaries. Finally, as an alternative to composition, we
prove privacy amplification results for contractive noisy iterations and
showcase the first use of Pufferfish in private convex optimization. A common
ingredient underlying our results is the use and extension of shift reduction
lemmas.
( 2
min )
